Social information decreases giving in late-stage fundraising campaigns

Online fundraisers often showcase information about the number of donations received and the proximity to the campaign goal. This practice follows research on descriptive norms and goal-directed motivation, which predicts higher contributions as the number of donors increases and as the campaign goal is approached. However, across three studies, we demonstrate that when the campaign is close to completion, individuals give more when they see that there are few (vs. many) donors to the campaign. We observe this result across real campaigns on a fundraising website and obtain causal evidence for this effect in two laboratory experiments. We find that this effect is driven in part by an increase in the perceived progress that one’s donation makes towards reaching the campaign goal. This work identifies a counterintuitive consequence of norm-based marketing appeals and has important implications for fundraisers.

• Following the suggestion of Reviewer 2, we have heavily revised the front end and the general discussion of the paper. We now discuss the existing literature more thoroughly, elaborate on the theoretical basis of our predictions, and explain the proposed mechanism more carefully. In the general discussion, we further discuss theoretical contributions of the results. • Given that the Kickstarter results raised major concerns by both reviewers, and the fact that the JGive data is a much richer dataset than the Kickstarter dataset, we did not include the Kickstarter study in the revised manuscript. • In response to Reviewer 2, we present results on the likelihood of giving in results section of each study. • We now publicly share the data, analysis scripts, and survey materials for studies 2 and 3.
In addition, we now include an Ethics Statement in the manuscript (p. 12) that is in compliance with the journal's guidelines.
Below, we present a point-by-point response to each comment provided by the two reviewers. Per the journal's requirements, we have highlighted major changes in the manuscript in blue text.
Thank you for the opportunity to revise this paper.

Sincerely, The Authors
Journal Requirements: When submitting your revision, we need you to address these additional requirements. Response: Our Ethics Statement (p. 12) now specifies that we are in compliance with these requirements. We have included this statement below for your convenience: All studies were approved (in writing) by the Human Research Protection Program of the University of California, San Diego (IRB number: 130572XX). Study 1 used data from the field and Studies 2 and 3 carried out at University of California, San Diego. Participants in Studies 2 and 3 were recruited from the Rady School of Management behavioral lab's subject pool and were undergraduate students who take part in laboratory studies for course credit. All participants in these studies provided written consent to participate. Study 1 data was provided directly by JGive with their permission to publish research findings and remained within their terms and conditions. Data, analysis scripts, and survey materials for Studies 2 and 3 are available on Open Science Framework (OSF) at https://osf.io/rb8s2/?view_only=80cf75c84fe940cf8860b6cb22633dde. Study 1 data is proprietary and cannot be shared publicly.
As explained below (see 5.1), we no longer present the Kickstarter study in the manuscript. We will update your Data Availability statement to reflect the information you provide in your cover letter.

In your
Response: Please use the following Data Availability statement: Data Availability: Study 1 data was provided directly by JGive with their permission to publish research findings and remained within their terms and conditions. Data, analysis scripts, and survey materials for Studies 2 and 3 are available on Open Science Framework (OSF) at https://osf.io/rb8s2/?view_only=80cf75c84fe940cf8860b6cb22633dde. Study 1 data is proprietary and cannot be shared publicly.
4. We note that you have stated that you will provide repository information for your data at acceptance. Should your manuscript be accepted for publication, we will hold it until you provide the relevant accession numbers or DOIs necessary to access your data. If you wish to make changes to your Data Availability statement, please describe these changes in your cover letter and we will update your Data Availability statement to reflect the information you provide.
Response: As indicated in our previous response, a data repository is available here: https://osf.io/rb8s2/?view_only=80cf75c84fe940cf8860b6cb22633dde A DOI will be provided at acceptance.

Please include your full ethics statement in the '
Methods' section of your manuscript file. In your statement, please include the full name of the IRB or ethics committee who approved or waived your study, as well as whether or not you obtained informed written or verbal consent. If consent was waived for your study, please include this information in your statement as well.
Response: As mentioned, a full ethics statement including this information has now been added to the main text (p. 12).
6. Please include a caption for figure 2.
Response: A caption has now been added to this figure. Response: You are correct that in Study 1a we look at the average donation of each project, rather than each incremental donation. Unfortunately, individual donations were not captured in this dataset. Your major concern about this data, together with the concerns raised by the other reviewer led us to decide not to present this study. We believe that the results from the Kickstarter data do not offer new insights other than a conceptual replication of Study 1b. Given that the JGive dataset is much richer than the Kickstarter dataset as it includes all individual donations, we believe that the results of the JGive analyses provide sufficient evidence from the field for our hypothesis. Response: It is our understanding that weighted least squares regression is most suitable when homoscedasticity assumption of constant variance in the error terms is violated. We were not sure whether this was your concern and whether weights of 1/N would necessarily alleviate it (but see below). Moreover, the over representation issue is more relevant in the former Study 1a, where we did not have individual donation information. If the concern is that some unobserved campaign characteristics that are unique to large campaigns may drive the results, we note that all our regressions include campaign random effects to account for such unobserved campaign-level variables. In fact, these are nested effects within charity random effects to account for the fact that a charity may run multiple campaigns over the investigated time frame. Note, having many donors in a campaign, holding other factors constant, is, in fact part of the effect we are trying to measure.

Minor:
As you suggested, we present below regressions using subsets of the data by decile of goal progress for projects with at least 40% of the goal reached (40% is the predicted inflection point, see Fig 1). As you can see in Table 1 below, all deciles revealed a negative effect of number of donors on the donation amount. We noticed however, that some coefficients did not reach significance level (although they were directional) and we suspected that observations of very large campaigns that are included in some deciles but not in others, may behave slightly differently as you suggested. When we reran the analysis, this time excluding 131 campaigns with a fundraising goal of one million NIS or more, the coefficients of all deciles became significant (Table 2). Finally, since the excluded large campaigns had significantly more donors (Mean = 64.4) than the rest of the campaigns (Mean = 11.4), the latter result addresses the concern that the study results are due to campaigns with many donors/observations receiving too much weight.
We hope that these results, together with the controlled studies, helped convincing you that our effect is real. Response: We appreciate this comment and will first address the predictions one can make based on existing literature, followed by the topic of pivotality. Recent empirical work on the effect of group-size on contribution to public goods reveals mixed results. In fact, several recent publications showed that group-size has a positive effect on cooperation (Barcelo and Capraro, 2015;Diederich, Goeschl, and Waichman, 2016;Pereda, Capraro, and Sánchez, 2019). Furthermore, as we note in the manuscript (p. 5), research in psychology on descriptive norms indicates that the number or percentage of individuals engaging in a behavior increases, others will follow that behavior (Cialdini, Kallgren, & Reno, 1991;Cialdini, Reno, & Kallgren, 1990;Kallgren, Reno, & Cialdini, 2000). Following your comment, however, we added discussion about the public goods problem to the introduction of the manuscript (p. 6).
Regarding the pivotality of the donation, we agree with your logic but offer and find a slightly broader interpretation. We believe that pivotality is another way to think of perceived progress and therefore this explanation is similar to ours. Indeed, we show that when learning that few (vs. many) people have donated, participants perceived their contribution to have a larger impact on helping the campaign reaching its goal, or as you described it, they felt that their contribution is more pivotal. We therefore don't see pivotality as an issue but rather as a different way to describe the results. Further, we note that pivotality is largest when one can complete the goal. While this is not an issue in the lab studies because the goal was unknown, larger final donations could potentially drive the results from the field. However, our field data results remain the same even when controlling for whether a donation was the last contribution to complete the goal (see Table 2 in the manuscript, p. 15).
Finally, we also considered the notion (and potential explanation) of participants making inferences about the average donation and then donating a similar amount. While we did not measure this inference directly in Study 2, we did in Study 3, and found that it did not drive our results.

Procedures: I am very worried about the deception in the material that the authors present to the subjects: where did they take the numbers 23/1923/86%/14% from?
Similarly, were the participants aware of the probability of their decisions being implemented? I consider the probability of 1/571 to be really negligible for individual decisions. The authors should have rather chosen fixed probability of 1/10 or 1/20.

Response:
The number of donors and goal progress used in Studies 2 and 3 were taken from pretests indicating that participants perceived 1923 as more donors than 23 (1 = a very small number, 7 = a very large number) and 86% as a greater goal completion rate than 14% (1 = very far, 7 = very close). The exact numbers are less important than whether there is a perceived difference in the size of these figures. In reality, many online fundraising campaigns do not "take-off" and the average number of donors is relatively small. For example, in the 2018 Giving Tuesday drives, the average number of donors per campaign was 21.8 (www.classy.org/giving-tuesday) and in 2016, the average number of donors per campaign in GoFundMe, the world's largest social fundraising platform, was 12.5 (https://pages.gofundme.com/giving-report-2016; In 2017, GoFundMe stopped sharing this information).
Regarding the probability of participants' decisions being implemented, participants were indeed not given this information. Incentivizing decisions on a probabilistic basis is a widely implemented strategy in behavioral science and many studies exclude information about the winning probabilities (e.g., Munichor & Steinhart 2015;Erlandsson et al. 2016, Perez, Steinhart, Grinstein, & Morren 2022. In fact, one review found that across studies, paying a subset of participants (versus all participants) produced little change in results (Charness, Gneezy, & Halladay 2016). Because our measure of giving was a one-shot decision that was not effort-or skill-dependent, we believe that participants would behave as if their decision would be implemented regardless of the probability of implementation. We note that all participants received course credit for their participation and did not expect an additional payment when showing up at the lab. Even if we used a fixed known probability of the decision being implemented, which could alter participants' belief about their winning odds, this would only affect their motivation to give (i.e., main effect). That is, we have no reason to believe that changing participants' perceived probability to win the lottery should differentially affect their contribution between the few and many donors conditions.

Study 3 Introduction: I am confused when the authors talk about study 2 and 3 that "participants viewed … only accumulated progress." Was Study 3 not aiming at changing this?
Response: Our apologies for the typo, we have now deleted 'Study 3' from this sentence.

My remarks to the procedures are the same as those to study 2.
Response: We hope that our responses above have helped to address your concerns about Study 3.

Do the authors correct for multiple hypothesis testing?
Response: A post-hoc Tukey test did not change the results as they remained statistically significant. We now discuss this test in the results section of Study 3. We also note that in Study 3, our mediation analyses included all potential explanations simultaneously, so the appropriate p-value correction is built into the test procedure.

Implications: I am very worried that the policy implications which the authors propose are not sufficiently backed up by the studies conducted. If the authors suggest that charities might split a large goal into smaller goals that could be achieved in smaller groups, why do not test this directly.
Response: We appreciate you pushing us to further demonstrate policy implications. However, suggestions of policy implications that are based on results from laboratory studies could always be criticized for lack of realism. We believe that testing the above policy implication in an additional field study would go beyond the scope of the current investigation. To alleviate your concern, in the new manuscript we also state that more studies should directly test this strategy in real fundraising campaigns. If this suggestion still raises this concern, we will be happy to remove it.
10. Overall, I am worried that the findings are quite trivial and in line with basis economic models of public good contributions and related to pivotality. Of course, one can test simple theory but the authors should spell it out at the beginning. If the results are not with the simple theory then it would be good to search for otherbehavioral-explanations. I suggest to drop Study 1a completely and move study 1b to the Appendix.
Response: As we note in our response to point 4, while our results are consistent with some economic models of public goods contributions, they run counter to recent public goods findings as well as to a large body of work on descriptive social norms. That is, while some public goods findings suggest that individuals will give less when others give more, other findings suggest the opposite. Moreover, social norm explanations suggest that individuals will give more when others give more. Given strong evidence that people largely conform to the behavior of others (e.g., social norms), we believe that our findings are not obvious.
You also mentioned that our results are in line with work on pivotality. As mentioned above, we don't see pivotality as an issue but rather as another contributor to the results. Note, however that we control for the 'completion effect' which is essentially pivotality as you describe it and still find the reported pattern of results. Moreover, our work generalizes beyond this by examining donations at late stages of campaigns where it may not be clear or plausible to donors that their donation will be the one to finish the project. Furthermore, to our knowledge, research on pivotality has not explored the joint impact of the percentage of the goal reached and number of previous donors, as we do in this research. Response: Thank you for the recommendation, we have now cited these papers in the manuscript (p. 8). Response: Your request was very well taken, and we now made the data, analysis scripts, and survey materials for studies 2 and 3 publicly available. They are available here: https://osf.io/rb8s2/?view_only=80cf75c84fe940cf8860b6cb22633dde

In addition
As you noted, the JGive data cannot be shared due to propriety reasons and the Kickstarter analyses was not shared because as you will see below, following the review team aggregated feedback, we decided not to present Study 1a. Regardless, Kickstarter data is available from the following website: https://webrobots.io/kickstarterdatasets.
2) Introducing the scope of the project. The introduction might make clearer, at the beginning, why the two variables under study (and their interaction effect)  Response: Thank you for this suggestion. Following your feedback, we completely rewrote the introduction section. We now discuss the existing literature more thoroughly, elaborate on the different predictions of these theories, and explain the proposed mechanism more carefully. We hope that you will find the new introduction to your liking. Finally, we note that both studies 2 and 3 also analyze the probability of giving and find similar results, but for brevity, we previously presented these results in the supplementary materials. We now present these analyses in the results section of each study, in the main text. Response: We agree that the theoretical grounding of the paper should be clearer and more detailed. We have now expanded the introduction, discussing how our work draws on the focus theory of normative conduct (Cialdini, Kallgren, & Reno, 1991;Cialdini, Reno, & Kallgren, 1990;Kallgren, Reno, & Cialdini, 2000). We have also provided a more thorough review of the social norms and fundraising literatures. Furthermore, we have now extended and clarified discussion of the potential mechanisms underlying the hypothesized effect. We hope that these changes now more effectively motivate our research question and hypotheses. Response: While several publications investigate prosocial behavior in the context of Kickstarter projects, and even demonstrate that contributors on Kickstarter have prosocial motivations to help creators reach their funding goals (e.g., Dai & Zhang 2019), we agree that whether reward-based crowdfunding platforms such as Kickstarter are a good context for studying prosocial behavior may still be questionable to many scholars. We therefore take your comment to heart. In addition, the validity of the Kickstarter data was a major concern of the other reviewer. Given that the JGive data is much richer than the Kickstarter data as it includes all individual donations, we believe that the results of the JGive analyses provide sufficient evidence from the field for our effect. Thus, we decided not to present the Kickstarter study in the revised manuscript.
Using student samples to study donation decisions is a common practice in many published papers as students often create, manage, and donate to fundraisers (e.g., Van Baaren et al., 2004, Frey & Meier, 2004Martin & Randal, 2009Siugmin et al., 2020. Since this was not a new practice, we did not think that explaining why students would be a suitable population to study our research question was necessary. Still, following your comment, we added language in the general discussion section highlighting the potential limitation of this approach (p. 30).
We agree that the donation rate in the lab experiments may seem higher than in normal giving contexts. However, two reasons may partly explain this result. First, students were offered to donate from an additional bonus which was on top of the expected course credit they received for their participation. Second, the donation decision was not an all-or-nothing decision but instead students could donate a portion of their $20 bonus. Indeed, the average donation was significantly smaller than $20 indicating that most participants did not donate the entire amount but instead kept some portion of the bonus for themselves. We note that the reported percentage of respondents giving money includes any donation greater than zero. Given that the findings from these experiments are supported by the data on real campaigns in the JGive data, we believe that the lab results are generalizable. Still, we now discuss this concern as a potential limitation (p. 30). Thank you for pointing out these important issues.

I hope my suggestions and questions help the authors to further improve their manuscript.
Thank you very much for your insightful feedback and support.